Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity

被引:2
作者
Chamberlain, Caden P. [1 ]
Meigs, Garrett W. [2 ,3 ]
Churchill, Derek J. [1 ,2 ]
Kane, Jonathan T. [1 ]
Sanna, Astrid [1 ]
Begley, James S. [4 ]
Prichard, Susan J. [1 ]
Kennedy, Maureen C. [5 ]
Bienz, Craig [6 ,7 ]
Haugo, Ryan D. [6 ]
Smith, Annie C. [2 ]
Kane, Van R. [1 ]
Cansler, C. Alina [8 ]
机构
[1] Univ Washington, Sch Environm & Forest Sci, Seattle, WA 98195 USA
[2] Washington State Dept Nat Resources, Olympia, WA USA
[3] Oregon State Univ, Coll Forestry, Corvallis, OR 97333 USA
[4] Washington Conservat Sci Inst, Roslyn, WA USA
[5] Univ Washington, Sch Interdisciplinary Arts & Sci, Tacoma, WA 98402 USA
[6] Nature Conservancy, Portland, OR 97214 USA
[7] Klamath Tribe, Chiloquin, OR 97624 USA
[8] Univ Montana, WA Franke Coll Forestry & Conservat, Missoula, MT 59812 USA
关键词
adaptive management; burn severity; framework; fuel reduction treatments; treatment effectiveness; wildfire; FUEL TREATMENTS; CLIMATE-CHANGE; FIRE SEVERITY; PRESCRIBED FIRE; FOREST; REDUCTION; LANDSCAPE; RESTORATION; PATTERNS; HISTORY;
D O I
10.1002/ecs2.70073
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Interruption of frequent burning in dry forests across western North America and the continued impacts of anthropogenic climate change have resulted in increases in fire size and severity compared to historical fire regimes. Recent legislation, funding, and planning have emphasized increased implementation of mechanical thinning and prescribed burning treatments to decrease the risk of undesirable ecological and social outcomes due to fire. As wildfires and treatments continue to interact, managers require consistent approaches to evaluate treatment effectiveness at moderating burn severity. In this study, we present a repeatable, remote sensing-based, analytical framework for conducting fire-scale assessments of treatment effectiveness that informs local management while also supporting cross-fire comparisons. We demonstrate this framework on the 2021 Bootleg Fire in Oregon and the 2021 Schneider Springs Fire in Washington. Our framework used (1) machine learning to identify key bioclimatic, topographic, and fire weather drivers of burn severity in each fire, (2) standardized workflows to statistically sample untreated control units, and (3) spatial regression modeling to evaluate the effects of treatment type and time since treatment on burn severity. The application of our framework showed that, in both fires, recent prescribed burning treatments were the most effective at reducing burn severity relative to untreated controls. In contrast, thinning-only treatments only produced low/moderate-severity effects under the more moderate fire weather conditions in the Schneider Springs Fire. Our framework offers a robust approach for evaluating treatment effects on burn severity at the scale of individual fires, which can be scaled up to assess treatment effectiveness across multiple fires. As climate change brings increased uncertainty to dry forest ecosystems of western North America, our framework can support more strategic management actions to reduce wildfire risk and foster resilience.
引用
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页数:22
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